store = {}
store['args']={'name': 'emnist_independent_bald_k10_335690', 'available_sample_k': 5, 'num_inference_samples': 10, 'seed': 335690, 'acquisition_method': 'AcquisitionMethod.independent', 'experiment_description': 'EMNIST with b5 and k10, k100 with both BALD and BatchBALD', 'type': 'AcquisitionFunction.bald', 'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 16384, 'early_stopping_patience': 3, 'epochs': 40, 'epoch_samples': 20224, 'target_accuracy': 0.85, 'target_num_acquired_samples': 300, 'log_interval': 20, 'dataset': 'DatasetEnum.emnist', 'initial_samples': [], 'experiment_task_id': 12, 'experiments_laaos': './experiment_configs/emnist_bbb/configs.py', 'no_cuda': False, 'quickquick': False, 'initial_samples_per_class': 2}
store['cmdline']=['./src/ignite_mnist.py', '--experiment_task_id=12', '--experiments_laaos=./experiment_configs/emnist_bbb/configs.py']
store['iterations']=[]
store['initial_samples']=[]
store['iterations'].append({'num_epochs': 0, 'test_metrics': {'accuracy': 0.020106382978723403, 'nll': 3.8618894968641566}, 'chosen_samples': [89935, 25477, 86150, 101625, 105021], 'chosen_samples_score': [0.013303232565522016, 0.01332994587719405, 0.013398011028766366, 0.01340008229017231, 0.013523744791745784], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.04436170212765957, 'nll': 39.105008999438994}, 'chosen_samples': [94296, 79152, 55926, 47150, 59318], 'chosen_samples_score': [1.200261454974584, 1.2004490514714947, 1.2511225219153994, 1.2454979150557313, 1.218704762691028], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.06138297872340426, 'nll': 33.93847733193255}, 'chosen_samples': [109221, 58663, 12078, 94626, 9140], 'chosen_samples_score': [1.2000316053127467, 1.2088157523052394, 1.2529360814011512, 1.2180581116193905, 1.2946454431162804], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.07212765957446808, 'nll': 41.61529741977124}, 'chosen_samples': [11546, 100680, 6218, 101991, 80270], 'chosen_samples_score': [1.1976373117945653, 1.208632307047631, 1.2329451931936024, 1.234190357971753, 1.343741286929375], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.09675531914893618, 'nll': 33.42860274132262}, 'chosen_samples': [42780, 10698, 107717, 31882, 11398], 'chosen_samples_score': [1.5584464717849085, 1.5660953617764486, 1.5785650168790615, 1.5988224067699073, 1.5724617854573417], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.13175531914893618, 'nll': 31.828906915542927}, 'chosen_samples': [91014, 65153, 6214, 32732, 16707], 'chosen_samples_score': [1.5710037638728087, 1.575881093679851, 1.6192380253883925, 1.5865910358525568, 1.6197209343272294], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.1373936170212766, 'nll': 34.67714982377722}, 'chosen_samples': [86112, 20777, 49963, 66467, 59917], 'chosen_samples_score': [1.6663365531328147, 1.6664373313480647, 1.6871060681678618, 1.7482752689151435, 1.6884906399385746], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.1475, 'nll': 27.078718399697163}, 'chosen_samples': [82514, 33285, 9954, 44115, 95926], 'chosen_samples_score': [1.5683891953009739, 1.5851390167886625, 1.598519244012848, 1.6341921305898257, 1.7169995992273743], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.151968085106383, 'nll': 30.7348574666774}, 'chosen_samples': [28528, 5226, 81047, 22831, 65112], 'chosen_samples_score': [1.546053367191967, 1.5739194729545667, 1.6100859023723553, 1.5816831888244098, 1.5600624338290572], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.16175531914893618, 'nll': 22.833843258796854}, 'chosen_samples': [92644, 90260, 97480, 5264, 53380], 'chosen_samples_score': [1.6392278633577435, 1.6436170862384227, 1.6492282010059531, 1.665398594184575, 1.6511402133161646], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.14351063829787233, 'nll': 24.43088618826359}, 'chosen_samples': [32891, 107976, 2028, 108560, 39636], 'chosen_samples_score': [1.6096149211188142, 1.6236714819730786, 1.6323140152702442, 1.6378049281497504, 1.6745064906241889], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.18420212765957447, 'nll': 22.418553864499355}, 'chosen_samples': [41476, 14318, 46071, 80817, 30921], 'chosen_samples_score': [1.6025443096320102, 1.6035798328103248, 1.627595679890515, 1.6379035571511222, 1.713709887603015], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.1895744680851064, 'nll': 24.474084115535653}, 'chosen_samples': [45417, 21592, 75077, 109859, 5059], 'chosen_samples_score': [1.6999341073834149, 1.7009656378802387, 1.7032827518806082, 1.7540582185260596, 1.7367823520102854], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.19590425531914893, 'nll': 21.515723037719727}, 'chosen_samples': [54557, 11605, 105074, 69413, 40363], 'chosen_samples_score': [1.6333551635318488, 1.647594390645477, 1.6641088526705043, 1.75229023903703, 1.6804285678867188], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.21058510638297873, 'nll': 24.570482525115317}, 'chosen_samples': [70797, 53816, 70072, 60586, 77798], 'chosen_samples_score': [1.6973286926848832, 1.719110863379457, 1.7413310987398416, 1.7588784517537255, 1.8013718397618848], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.24473404255319148, 'nll': 21.074699612881275}, 'chosen_samples': [36919, 89574, 100047, 80945, 78013], 'chosen_samples_score': [1.6333024071151994, 1.6353605149163326, 1.6398460787383873, 1.7193899810040614, 1.6552925597671617], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.22680851063829788, 'nll': 20.068781788602788}, 'chosen_samples': [41104, 25939, 86396, 55929, 4422], 'chosen_samples_score': [1.5530343470102959, 1.556772418919155, 1.5598574068361717, 1.6814988362078696, 1.5700654243218422], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.2520212765957447, 'nll': 18.47793897425875}, 'chosen_samples': [22600, 104485, 52041, 61813, 65408], 'chosen_samples_score': [1.6919873871891167, 1.7008832483665677, 1.7227937455019458, 1.7516934949371668, 1.711985355052497], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.25930851063829785, 'nll': 15.330450384261761}, 'chosen_samples': [9778, 64493, 84602, 110294, 73069], 'chosen_samples_score': [1.6709060284808266, 1.6891283622618296, 1.7522749578619106, 1.7774329625114653, 1.7866516738983365], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2578191489361702, 'nll': 13.425420669393336}, 'chosen_samples': [23952, 11706, 110536, 48210, 80627], 'chosen_samples_score': [1.5475821217995342, 1.5479698901427619, 1.5732576247851502, 1.573578064244451, 1.6489267972897725], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2910106382978723, 'nll': 13.330064878260835}, 'chosen_samples': [38629, 70385, 26083, 66551, 63431], 'chosen_samples_score': [1.61450921194404, 1.6183795661259053, 1.6244273770251052, 1.6413970932855233, 1.7246379465939092], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2747340425531915, 'nll': 14.033449864488967}, 'chosen_samples': [108207, 12004, 46698, 97820, 108885], 'chosen_samples_score': [1.6724173420214243, 1.6861858494815283, 1.6978196101703826, 1.755466258896593, 1.7024253566916745], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.29106382978723405, 'nll': 12.950305364892838}, 'chosen_samples': [58774, 84879, 76127, 65636, 39564], 'chosen_samples_score': [1.5134408100628898, 1.5833594812751473, 1.5499919189399955, 1.5209839985457423, 1.7278533416581459], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2849468085106383, 'nll': 13.206156575629052}, 'chosen_samples': [94847, 51225, 36416, 94684, 74879], 'chosen_samples_score': [1.5867876628704547, 1.602069589993334, 1.7321793534298358, 1.6162052603152113, 1.6387013895564355], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.2748404255319149, 'nll': 12.288270216596887}, 'chosen_samples': [65953, 97125, 90628, 100279, 52074], 'chosen_samples_score': [1.5585661950887286, 1.5659938447744237, 1.5738944251233957, 1.6335517251040297, 1.5908474151272811], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.29127659574468084, 'nll': 13.478721276141227}, 'chosen_samples': [108395, 38796, 108591, 60968, 2706], 'chosen_samples_score': [1.7162870101513665, 1.735071571068003, 1.7423046931773654, 1.7927372040412157, 1.746530119662976], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.31111702127659574, 'nll': 12.146355768568972}, 'chosen_samples': [103450, 101399, 51977, 59894, 33415], 'chosen_samples_score': [1.7029402117227126, 1.7059088950025865, 1.7072353917515626, 1.756170345782673, 1.822811833489381], 'chosen_samples_orignal_score': None})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3078723404255319, 'nll': 11.955566215515137}, 'chosen_samples': [1991, 65537, 42316, 70588, 96571], 'chosen_samples_score': [1.644217084976559, 1.648590495860161, 1.7757611688989283, 1.7108993178815242, 1.6943438739225472], 'chosen_samples_orignal_score': None})
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store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.3075, 'nll': 11.198503154186492}, 'chosen_samples': [74578, 101902, 55759, 34974, 106291], 'chosen_samples_score': [1.6738113995033483, 1.6804938345781195, 1.6873632815834827, 1.721602291188666, 1.7012681370735319], 'chosen_samples_orignal_score': None})
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